Life expectancy has reached a plateau in most developed countries and the incidence of chronic and life-threatening diseases is increasing, highlighting the need of novel therapies to cure them. However, the productivity of drug discovery R&D has been steadily declining. In fact, the inflation-adjusted research and development costs for a new drug has increased 100-fold between 1950 and 2010—roughly doubling every 9 years. In contrast to the semiconductor industry, where Moore’s law has shown that chip integration / performance doubles every two years, this decline in pharma R&D has been named Eroom’s law (“Moore” in reverse order). Nowadays, the costs of new drug development have increased from €1 billion in 2000 to €2.58 billion in 2015. The increase is due in part to the difficulty to find New Molecular Entities (NME) subject to be patented. This poses a serious threat to continue developing new therapies for unmet medical needs.
Drug development projects are long processes that take approximately 15 years from the moment a molecular target is discovered to the drug being approved to be used by patients. The process is divided in a pre-clinical and clinical phase with subsequent sub-phases, and in each one of those, huge amounts of money are invested. However, the success rates are very low since the suitability of molecules is difficult to predict. The goal of a preclinical drug discovery program is to deliver one or more clinical candidate molecules, each of which has enough evidence of biologic activity as well as sufficient safety and drug-like properties so that it can be entered into human testing. If the results of a phase are not optimal enough, the preclinical sub-phases must be repeated depending on the cause of failure, meaning higher costs, lost time and longer suffering patients.
Late preclinical and clinical stages are very costly and failing at these points significantly increases the project duration and investment. For this reason it is crucial for the pharmaceutical/biotech sector to move towards a lean drug discovery model where more design cycles are performed in the early stages of the development process (agile, flexible and cheap), accurately predicting, testing, learning and failing fast (if necessary) and feeding subsequent expensive stages with molecules that have higher chances to become a drug.
Computer-Aided Drug Design (CADD), also known as in-silico drug design, has become a powerful technique in the initial phases of drug discovery but has focused mainly in specific aspects of the drug alone, being the most optimized property the activity against the target of interest. However, the high attrition rates (96%) of drug discovery projects indicates that they are not identifying strong candidate molecules to be used at later stages. New CADD methodologies will have a key role in these initial stages to enable focused compound testing and optimization. Intelligent in-silico screening of huge virtual libraries (virtual screening) enables fast and affordable design cycles that can generate valuable experimental data and learnings to improve an iterative cascade screening process. PharmScreen2, the platform developed in this project, is based on an integrated HPC platform combined with ML and QM algorithms, allowing researchers to perform advanced simulations and to find the best new candidate molecules with a much higher potential to become new drugs.
